:nosearch:

.. _op_ai_onnx_BatchNormalization-9:

BatchNormalization - version 9
==============================

This page documents version **9** of operator **BatchNormalization**. See :doc:`BatchNormalization` for the latest version (since version 15).

- **Domain**: ``ai.onnx``
- **Since version**: 9

Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:

Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)

For previous (depreciated) non-spatial cases, implementers are suggested
to flatten the input shape to (N x C\*D1\*D2 ..\*Dn) before a BatchNormalization Op.

**Inputs**

- **X** (*T*): Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1
- **scale** (*T*): Scale tensor of shape (C).
- **B** (*T*): Bias tensor of shape (C).
- **mean** (*T*): running (training) or estimated (testing) mean tensor of shape (C).
- **var** (*T*): running (training) or estimated (testing) variance tensor of shape (C).

**Outputs**

- **Y** (*T*): The output tensor of the same shape as X
- **mean** (*T*): The running mean after the BatchNormalization operator.
- **var** (*T*): The running variance after the BatchNormalization operator.
- **saved_mean** (*T*): Saved mean used during training to speed up gradient computation.
- **saved_var** (*T*): Saved variance used during training to speed up gradient computation.

**Type Constraints**

- **T**: Constrain input and output types to float tensors.
  Allowed types: tensor(double), tensor(float), tensor(float16).

Differences with previous version (7)
-------------------------------------

**SchemaDiff**: ``BatchNormalization`` (domain ``'ai.onnx'``)

* old version: 7
* new version: 9
* breaking: no

**Documentation:**

* line similarity: 0.22 (+8/-6 lines)

.. code-block:: diff

    --- BatchNormalization v7
    +++ BatchNormalization v9
    @@ -1,8 +1,10 @@
     
    -    Carries out batch normalization as described in the paper
    -    https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
    -    there are multiple cases for the number of outputs, which we list below:
    +Carries out batch normalization as described in the paper
    +https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
    +there are multiple cases for the number of outputs, which we list below:
     
    -    Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
    -    Output case #2: Y (test mode)
    -        
    +Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
    +Output case #2: Y (test mode)
    +
    +For previous (depreciated) non-spatial cases, implementers are suggested
    +to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op.